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Found 10 papers, 7 papers with code
Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars
The best result for YOLOXs model with tiling strategy is 72. 3 mAP. 5, while the best result without tiling strategy is 71. 2.
Annealing-Based Label-Transfer Learning for Open World Object Detection
To the best of our knowledge, this is the first OWOD work without manual unknown selection.
Revisiting Open World Object Detection
Open World Object Detection (OWOD), simulating the real dynamic world where knowledge grows continuously, attempts to detect both known and unknown classes and incrementally learn the identified unknown ones.
Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection
In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102, 928 common prohibited items of 8 categories.
A Comprehensive Evaluation Framework for Deep Model Robustness
To mitigate this problem, we establish a model robustness evaluation framework containing 23 comprehensive and rigorous metrics, which consider two key perspectives of adversarial learning (i. e., data and model).
Spatiotemporal Attacks for Embodied Agents
Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness.
Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module
Furthermore, to deal with the occlusion in X-ray images detection, we propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors.
Stratified Rule-Aware Network for Abstract Visual Reasoning
We further point out the severe defects existing in the popular RAVEN dataset for RPM test, which prevent from the fair evaluation of the abstract reasoning ability.
Region-wise Generative Adversarial ImageInpainting for Large Missing Areas
To address these problems, we propose a generic inpainting framework capable of handling with incomplete images on both continuous and discontinuous large missing areas, in an adversarial manner.
Interpreting and Improving Adversarial Robustness of Deep Neural Networks with Neuron Sensitivity
In this paper, we first draw the close connection between adversarial robustness and neuron sensitivities, as sensitive neurons make the most non-trivial contributions to model predictions in the adversarial setting.
